Bars and restaurants are ubiquitous. Bar districts in cities often have dozens of bars and restaurants from which patrons can choose. Patrons typically desire to frequent bars and restaurants that have a high level of activity, i.e., generally speaking, bars and restaurants that are popular or “happening.” On the other hand, some people prefer to go to bars and restaurants that have a low level of activity, i.e., generally uncrowded. Determining which bars or restaurants to visit can be challenging given the large number of venues from which to choose. Promotions or events at bars and restaurants are also important considerations for patrons when deciding on which venue to frequent.
Numerous websites and mobile applications provide listings of various bars and restaurants. Typically, such systems provide lengthy listing of all bars and restaurants in a metropolitan area. A disadvantage of such systems is that it takes a long time for a user to review the lengthy list of venues, and even then, the information provided is not sufficient to meaningfully differentiate venues. Another disadvantage of such systems is that they rely exclusively on manually-entered data that is infrequently updated. Thus, the data is often outdated and inaccurate. Still yet another disadvantage of such systems is that they do not provide information on bar districts or areas. Thus, such systems have limited utility.
Accordingly, patrons lack useful means for determining what are the “hot” districts and the “happening” venues within a metropolitan area. Patrons thus resort to word-of-mouth methods, e.g., text messaging or calling a friend and asking for recommendations. However, such word-of-mouth methods are inefficient the requesting party must manually provide information on their criteria. Word-of-mouth methods are further disadvantageous as they are only capable of providing information on a handful of venues of which a friend has knowledge. And, unless the friend happens to be present at the recommended venue, any information provided does not reflect current conditions. Word-of-mouth methods are also unreliable. The friends often does not know the name or location of the recommended venue. Moreover, such methods are unavailable when most needed and desired, e.g., when a person is travelling or vacationing to a new city.
As such, there is a need for a system for providing patrons with information on whether a venue is “happening” or uncrowded. Further, there is a need for such a system to provide accurate information that reflect current levels of activity. Still further, there is a need for such a system to provide information on promotions and events. Still further, there is a need for such a system to provide information on venues in a useful and intuitive manner that allows patrons to identify nearby venues and districts. Still further, there is a need for robust system that uses real-time data from a plurality of sources.